Determining merchant enforced transaction rules

ABSTRACT

Systems as described herein determine merchant enforced transaction rules. A determination server may receive transaction data associated with a plurality of merchants. The determination server may generate a histogram of payments associated with a merchant category and filter out transaction data having purchase amounts above or below a predetermined threshold. The determination server may determine a first average purchase amount associated with merchants in the merchant category and a second average purchase amount associated with each merchant in the merchant category. The determination server may determine user spending patterns and that a first merchant in the merchant category enforces one or more card-based transaction rules using machine learning models. After determining that a user device is proximately located to the first merchant, a notification indicating the one or more card-based transaction rules associated with the first merchant may be sent to the user device.

FIELD OF USE

Aspects of the disclosure relate generally to big data and morespecifically to the processing and management of big data.

BACKGROUND

In an electronic payment processing network, a financial institution mayreceive transaction data originated from a variety of merchants,including small business merchants. The merchants may enforce certaincard-based transaction rules, such as a requirement of a minimumpurchase amount, or a maximum purchase amount for debit card or creditcard-based transactions. The customers may not be aware of these rulesuntil they are ready to make the payment. As a result, the customers mayhave to, for example, either pay a surcharge or to make additionalpurchase to meet the minimum purchase amount requirement. Conventionalfinancial systems may not have a mechanism to readily identify theserules antecedently, thereby limiting their ability to provide insightsto the transactions and facilitate their customers to make informeddecisions.

Aspects described herein may address these and other problems, andgenerally improve the quality, efficiency, and speed of processing bigdata to offer insights into merchants that enforce card-basedtransaction rules so that a notification may be sent to the customerswhen they come into the vicinity of such merchants.

SUMMARY

The following presents a simplified summary of various aspects describedherein. This summary is not an extensive overview, and is not intendedto identify key or critical elements or to delineate the scope of theclaims. The following summary merely presents some concepts in asimplified form as an introductory prelude to the more detaileddescription provided below. Corresponding apparatus, systems, andcomputer-readable media are also within the scope of the disclosure.

Systems as described herein may include features for determiningmerchant enforced transaction rules. A determination system may receivetransaction data associated with a plurality of merchants and users. Ahistogram of payments associated with a merchant category may begenerated based on the transaction data. The determination system mayfilter out transaction data that have purchase amounts above or below apredetermined threshold. The determination system may determine a firstaverage purchase amount associated with merchants in the merchantcategory, and a second average purchase amount associated with eachmerchant in the merchant category. The determination system maydetermine spending patterns associated with the plurality of users usinga first machine learning model. Based on the spending patterns and thesecond average purchase amount, the determination system may determinethat a first merchant in the merchant category enforces one or morecard-based transaction rules determining using a second machine learningmodel. Responsive to a determination that a user device associated witha first user is proximately located to the first merchant, anotification may be sent to the user device indicating the one or morecard-based transaction rules associated with the first merchant.

These features, along with many others, are discussed in greater detailbelow.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is described by way of example and not limited inthe accompanying figures in which like reference numerals indicatesimilar elements and in which:

FIG. 1 shows an example of a system for determining merchant enforcedtransaction rules in which one or more aspects described herein may beimplemented;

FIG. 2 shows an example computing device in accordance with one or moreaspects described herein;

FIG. 3 shows a flow chart of a process for determining merchant enforcedtransaction rules according to one or more aspects of the disclosure;

FIGS. 4A-4B show example histograms of payments for determining merchantenforced transaction rules according to one or more aspects of thedisclosure;

FIG. 5 shows a flow chart of a process for determining other merchantenforced transaction rules according to one or more aspects of thedisclosure

FIGS. 6A-6B show example search pages for determining merchant enforcedtransaction rules according to one or more aspects of the disclosure;and

FIGS. 7A-7B show example notifications displayed on a user deviceaccording to one or more aspects of the disclosure.

DETAILED DESCRIPTION

In the following description of the various embodiments, reference ismade to the accompanying drawings, which form a part hereof, and inwhich is shown by way of illustration various embodiments in whichaspects of the disclosure may be practiced. It is to be understood thatother embodiments may be utilized and structural and functionalmodifications may be made without departing from the scope of thepresent disclosure. Aspects of the disclosure are capable of otherembodiments and of being practiced or being carried out in various ways.In addition, it is to be understood that the phraseology and terminologyused herein are for the purpose of description and should not beregarded as limiting. Rather, the phrases and terms used herein are tobe given their broadest interpretation and meaning.

By way of introduction, aspects discussed herein may relate to methodsand techniques for determining merchant enforced transaction rules. Thecard-based transaction rules may include a minimum purchase amount, amaximum purchase amount, a surcharge for card-based transactions, and/ora restriction on acceptance of certain credit cards or debit cards. Thedetermination system may detect a pattern associated with a plurality oftransactions that each share a common payment amount or a payment amountabove a predetermined amount (i.e., there are no payments below acertain amount). The determination system may determine that the firstmerchant in the merchant category enforces the one or more card-basedtransaction rules based on the common payment amount or the paymentamount above a predetermined amount.

The determination system as described herein allows for receiving, ageographic location associated with the user device, and comparing thegeographic location with a predefined geofence associated with the firstmerchant. The determination system may also detect that the user devicehas connected to a wireless network associated with the first merchantfor a predetermined period of time.

In many aspects, the determination system may train the first machinelearning model using transaction data associated with one or moremerchants in the merchant category. The determination system may trainthe second machine learning model using transaction data associated withmerchants of similar size of the first merchant and/or located in ageographic area of the first merchant. The determination may receive ageographic location associated with the user device, and send a secondnotification to the user device indicating a plurality of merchants inthe area of the geographic location that do not enforce the one or morecard-based transaction rules.

Rule Determination Systems

FIG. 1 shows an example of a system 100 where the card-based rules maybe determined. The system 100 may include one or more merchant devices110, one or more user devices 120, at least one determination server130, at least one transaction record database 140, and/or at least oneenterprise merchant intelligence (EMI) database 150 via a network 160.It will be appreciated that the network connections shown areillustrative and any means of establishing a communications link betweenthe computers may be used. The existence of any of various networkprotocols such as TCP/IP, Ethernet, FTP, HTTP and the like, and ofvarious wireless communication technologies such as GSM, CDMA, WiFi, andLTE, is presumed, and the various computing devices described herein maybe configured to communicate using any of these network protocols ortechnologies. Any of the devices and systems described herein may beimplemented, in whole or in part, using one or more computing systemsdescribed with respect to FIG. 2 .

Merchant devices 110 may submit transaction information related to atransaction such as a merchant identifier, a transaction amount, amerchant identifier, a transaction location, and/or a transactiontimestamp. Merchant devices 110 may send requests for authorization fortransactions for payments that may be subject to card-based transactionrules. Some merchant devices 110 may be a Point of Sale (POS) devicelocated at a small business merchant, such as a convenience store, acoffee shop, a gas station, a farmer's market, etc. These small businessmerchants may enforce some card-based transaction rules including, forexample, a requirement for a minimum purchase amount, a surcharge forcredit card-based transactions, or an acceptance of certain credit cards(e.g. Visa Card), but not other credit cards (e.g. American Express).Some merchant devices 110 may be located at a merchant such as a cardealership or a university that may process transactions related to carpayment or tuition payment. These merchant (e.g. car dealership,university) may enforce rules such as a maximum amount allowable forcredit card-based transactions, or an acceptance of only certain creditcards.

User devices 120 may be any device that belongs to a customer of afinancial institution. The customers may conduct transactions withmerchant devices 110 using user devices 120 and/or the card issued bythe financial institution. For example, a customer may make an onlinetuition payment using user devices 120. User devices 120 may send ageographic location to determination server 130 so that thedetermination server 130 may be aware that user devices 110 may beproximately located to, or within a geo-fence associated with, amerchant. User devices may connect to a wireless network associated witha merchant when the user devise 110 may be within the vicinity of amerchant. User devices 110 may receive a notification whether one ormore merchant may enforce card-based transaction rules. For example,user devices 110 may display a map showing a current location of theuser devices 110 and several merchants that may be proximately locatedto the user devices 110. User devices 110 may receive a notificationindicating the merchants that may enforce card-based transaction rulesand may display such merchants on the map with corresponding labels.

User devices 110 may include computing devices, such as laptopcomputers, desktop computers, mobile devices, smart phones, tablets, andthe like. According to some examples, user devices 110 may includehardware and software that allow them to connect directly to network160. Alternatively, user devices 110 may connect to a local device, suchas a personal computer, server, or other computing device, whichconnects to network 160.

Determination server 130 may receive transaction data from merchantdevices 110 from a plurality of merchants. Determination server 130 mayretrieve merchant category information from a merchant database, such asenterprise merchant intelligence (EMI) database 150. The merchantcategory information may include a merchant category code (MCC) toclassify a merchant by the types of goods and/or services it provides.Determination server 130 may generate a histogram of payments associatedwith a merchant category based on the transaction data. For example,determination server 130 may identify a merchant category of interest,such as a merchant category of convenience stores, or car dealers.Determination server 130 may generate a histogram of payments for thebands or ranges of payments for the merchants in the merchant categoryof convenience stores. To determine whether the convenience storesenforce a minimum or maximum purchase amount, determination server 130may filter out any payment information that is, for example, above $10and analyze the payment data in the lower end of the histogram forpurchases made in convenience stores. Determination server 130 mayfilter out any payment information that is, for example, below $5000 andanalyze the payment data in the higher end of the histogram forpurchases made in car dealers. Determination server 130 may determine afirst average purchase amount associated with merchants in the merchantcategory. The first average purchase amount may be a minimum or amaximum charge amount that the merchants in the merchant category mayimpose for card-based transactions. Based on the first average purchaseamount, determination server 130 may determine a second average purchaseamount associated with each merchant in the merchant category. Thesecond average purchase amount may be a minimum or a maximum chargeamount that a particular merchant may impose for card-basedtransactions.

Determination server 130 may determining spending patterns associatedwith the plurality of users using a first machine learning model. Forexample, determination server 130 may determine a pattern in a pluralityof transactions, that each share a common payment amount. Based on thespending patterns and based on the second average purchase amount,determination server 130 may determine that a first merchant in themerchant category enforces one or more card-based transaction rulesusing a second machine learning model.

Determination server 130 may receive geographic location informationfrom user devices 120 and determine that a user device, may beproximately located to, or within a geo-fence of, the first merchant.Determination server 130 may send to the user device a notificationindicating the one or more card-based transaction rules associated withthe first merchant. Determination server 130 may also send to the userdevice a notification indicating other merchants who do not enforcecard-based transaction rules.

Transaction database 140 may store transaction records related totransactions previously conducted by users in transaction streams from aplurality of merchants. Transaction database 140 may receive a requestfrom determination server 130 and retrieve the corresponding transactionrecords to generate the histogram of payments. The transaction recordsmay each contain an account identifier, a transaction amount, atransaction time, a merchant identifier, etc. Transaction database 140may store transaction records from merchants that may enforce one ormore card-based transaction rules, such as a minimum purchase amount, amaximum purchase amount, or a surcharge for using a credit card or debitcard.

Enterprise merchant intelligence (EMI) database 150 may store merchantrecords related to various merchants, including small businessmerchants. EMI database 150 may be a merchant database that storesenterprise merchant intelligence records, which may in turn include amerchant identifier, a friendly merchant name, a zip code, a physicaladdress, a phone number, an email or other contact information of themerchants, and/or a merchant category code (MCC). A MCC may be afour-digit number listed in ISO 18245 for retail financial services andused to classify a business by the types of goods and/or services itprovides. MCCs may be assigned either by merchant type (e.g., one forhotels, one for office supply stores, etc.) or by merchant name. Forexample, convenience stores are classified as MCC No. 5499, “MISC FoodStores—Default,” car dealers are classified as MCC No. 5511, “Car &Truck Dealers/New/Used,” and universities are classified as MCC No.8229, “Colleges/UNIV/JC/Profession.” The merchant records may becollected from public resources and/or merchant reported records.

In a variety of embodiments, a financial organization may build aproprietary EMI database 150, for example, based on an aggregation oftransaction records in transaction database 150. As a transactionarrives from a transaction stream, the corresponding transaction recordmay be processed, cleaned, and/or enhanced with a variety of services.For example, when a financial institution receives the transactioninformation in a transaction stream, the transaction information may bein the form of a line of data that offers limited information about thetransaction, with each piece of information appearing in certainlocations within the line of data. The merchant identifier may appear ina specific location and may include 8-10 characters in the abbreviatedform, which may not be readily recognizable as a meaningful merchantname, particularly for small business merchants. The financialinstitution may process this abbreviated merchant identifier and convertit into a meaningful merchant name in a human readable format, and storeit in EMI database 150.

In a variety of embodiments, a financial institution may use a thirdparty API to gather merchant information, such as a merchant address orcontact information, to be stored in EMI database 150. In a variety ofembodiments, a financial organization may maintain more static merchantinformation, such as a merchant identifier and MCC, in its proprietaryEMI database 150; and a financial institution may use the third-partyAPI to get merchant address, merchant social media handle, or othermerchant information that may change over time. Determination server 130may store in EMI database 150, one or more card-based transaction rulesassociated with certain merchants.

Merchant devices 110, user devices 120, determination server 130,transaction database 140, and/or EMI database 150 may be associated witha particular authentication session. Determination 130 may receive,process, and/or store a variety of transaction records, enterprisemerchant intelligence information and card-based transaction rules,and/or receive transaction records with merchant devices 110 asdescribed herein. However, it should be noted that any device in system100 may perform any of the processes and/or store any data as describedherein. Some or all of the data described herein may be stored using oneor more databases. Databases may include, but are not limited torelational databases, hierarchical databases, distributed databases,in-memory databases, flat file databases, XML databases, NoSQLdatabases, graph databases, and/or a combination thereof. The network160 may include a local area network (LAN), a wide area network (WAN), awireless telecommunications network, and/or any other communicationnetwork or combination thereof.

The data transferred to and from various computing devices in system 100may include secure and sensitive data, such as confidential documents,customer personally identifiable information, and account data.Therefore, it may be desirable to protect transmissions of such datausing secure network protocols and encryption, and/or to protect theintegrity of the data when stored on the various computing devices. Afile-based integration scheme or a service-based integration scheme maybe utilized for transmitting data between the various computing devices.Data may be transmitted using various network communication protocols.Secure data transmission protocols and/or encryption may be used in filetransfers to protect the integrity of the data such as, but not limitedto, File Transfer Protocol (FTP), Secure File Transfer Protocol (SFTP),and/or Pretty Good Privacy (PGP) encryption. In many embodiments, one ormore web services may be implemented within the various computingdevices. Web services may be accessed by authorized external devices andusers to support input, extraction, and manipulation of data between thevarious computing devices in the data sharing system 100. Web servicesbuilt to support a personalized display system may be cross-domainand/or cross-platform, and may be built for enterprise use. Data may betransmitted using the Secure Sockets Layer (SSL) or Transport LayerSecurity (TLS) protocol to provide secure connections between thecomputing devices. Web services may be implemented using the WS-Securitystandard, providing for secure SOAP messages using XML encryption.Specialized hardware may be used to provide secure web services. Securenetwork appliances may include built-in features such ashardware-accelerated SSL and HTTPS, WS-Security, and/or firewalls. Suchspecialized hardware may be installed and configured in system 100 infront of one or more computing devices such that any external devicesmay communicate directly with the specialized hardware.

Computing Devices

Turning now to FIG. 2 , a computing device 200 that may be used with oneor more of the computational systems is described. The computing device200 may include a processor 203 for controlling overall operation of thecomputing device 200 and its associated components, including RAM 205,ROM 207, input/output device 209, communication interface 211, and/ormemory 215. A data bus may interconnect processor(s) 203, RAM 205, ROM207, memory 215, I/O device 209, and/or communication interface 211. Insome embodiments, computing device 200 may represent, be incorporatedin, and/or include various devices such as a desktop computer, acomputer server, a mobile device, such as a laptop computer, a tabletcomputer, a smart phone, any other types of mobile computing devices,and the like, and/or any other type of data processing device.

Input/output (I/O) device 209 may include a microphone, keypad, touchscreen, and/or stylus through which a user of the computing device 200may provide input, and may also include one or more of a speaker forproviding audio output and a video display device for providing textual,audiovisual, and/or graphical output. Software may be stored withinmemory 215 to provide instructions to processor 203 allowing computingdevice 200 to perform various actions. Memory 215 may store softwareused by the computing device 200, such as an operating system 217,application programs 219, and/or an associated internal database 221.The various hardware memory units in memory 215 may include volatile andnonvolatile, removable and non-removable media implemented in any methodor technology for storage of information such as computer-readableinstructions, data structures, program modules, or other data. Memory215 may include one or more physical persistent memory devices and/orone or more non-persistent memory devices. Memory 215 may include, butis not limited to, random access memory (RAM) 205, read only memory(ROM) 207, electronically erasable programmable read only memory(EEPROM), flash memory or other memory technology, optical disk storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium that may be used to storethe desired information and that may be accessed by processor 203.

Communication interface 211 may include one or more transceivers,digital signal processors, and/or additional circuitry and software forcommunicating via any network, wired or wireless, using any protocol asdescribed herein.

Processor 203 may include a single central processing unit (CPU), whichmay be a single-core or multi-core processor, or may include multipleCPUs. Processor(s) 203 and associated components may allow the computingdevice 200 to execute a series of computer-readable instructions toperform some or all of the processes described herein. Although notshown in FIG. 2 , various elements within memory 215 or other componentsin computing device 200, may include one or more caches including, butnot limited to, CPU caches used by the processor 203, page caches usedby the operating system 217, disk caches of a hard drive, and/ordatabase caches used to cache content from database 221. For embodimentsincluding a CPU cache, the CPU cache may be used by one or moreprocessors 203 to reduce memory latency and access time. A processor 203may retrieve data from or write data to the CPU cache rather thanreading/writing to memory 215, which may improve the speed of theseoperations. In some examples, a database cache may be created in whichcertain data from a database 221 is cached in a separate smallerdatabase in a memory separate from the database, such as in RAM 205 oron a separate computing device. For instance, in a multi-tieredapplication, a database cache on an application server may reduce dataretrieval and data manipulation time by not needing to communicate overa network with a back-end database server. These types of caches andothers may be included in various embodiments, and may provide potentialadvantages in certain implementations of devices, systems, and methodsdescribed herein, such as faster response times and less dependence onnetwork conditions when transmitting and receiving data.

Although various components of computing device 200 are describedseparately, functionality of the various components may be combinedand/or performed by a single component and/or multiple computing devicesin communication without departing from the invention.

Determining Merchant Enforced Transaction Rules

The determination system may process transaction data related to tens ofthousands of merchants, including small business merchants to generate ahistogram of payments. The determination system may analyze thehistogram of payments and further determine user spending patterns usingmachine learning models. The determination system may determine whethera merchant enforces one or more card-based transaction rules. Thedetermination system may send a notification to a user device that themerchant may enforce card-based transaction rules, or provide optionsfor other merchants who do not enforce such transactions rules, upon adetermination that the user device is proximately located to themerchant.

FIG. 3 shows a flow chart of a process for determining merchant enforcedtransaction rules according to one or more aspects of the disclosure.Some or all of the steps of process 300 may be performed using one ormore computing devices as described herein.

At step 310, the determination server may receive transaction dataassociated with a plurality of merchants. The transaction data may begenerated from a plurality of users and/or received via an electronicpayment network. Many merchants may have a general agreement withfinancial institutions that the merchants may not charge an extra feefor the customers to use a credit or debit card or impose a minimumpurchase amount for using the cards. However, some merchants may stillenforce such card-based transaction rules, particularly some smallbusiness merchants, such as a convenience store, a coffee shop, a gasstation, a farmer's market, etc. For example, in a farmer's market, themerchant may have a stall selling vegetables, and the profit margin maybe relatively low. So the merchant may not wish to accept credit cardfor purchases under $5. Some other merchants such as a car dealer, ajewelry store, or a university may impose a maximum purchase amount ontransactions using a credit card. For example, in a used car dealer, acustomer may purchase a used car for $11,000. The dealer may allow thecustomer to charge, at a maximum, $3000 on the credit card and theremaining balance to be provided with a personal check. In anotherexample, a college student may use a credit card to pay a portion of hertuition and the university may impose a rule for a maximum amount of$5000 to be charged on the credit card. The university may have furtherrestrictions, that only a Visa or Master Card may be accepted, but not aDiscover Card, an American Express Card or any international card. Assuch, merchant enforced card-based transaction rules may not be readilyvisible to the financial institutions, and their customers may not bewell-informed or offered alternative options to deal with such rulesahead of time.

In a variety of embodiments, the transaction data may be retrieved froma transaction database maintained by a financial institution. Thetransaction data may include a transaction identifier, a transactionamount, a merchant identifier, a transaction location, and/or atransaction timestamp. In some examples, the transaction data may bereceived from a customer when a receipt of the transaction was uploadedto the financial institution. The financial institution may ask thecustomer to provide a confirmation whether the merchant enforces anycard-based transaction rules.

In a variety of embodiments, the determination server may retrievetransaction data related to merchants in a certain merchant category.The determination server may retrieve transaction record containing amerchant identifier from the transaction database. The determinationserver may also retrieve the MCC related to the merchant identifier froman EMI database. As discussed previously, the MCC may identify amerchant by the types of goods and/or services it provides. For example,the convenience stores may be classified as MCC No. 5499, the cardealers may be classified as MCC No. 5511 and the universities may beclassified as MCC No. 8229.

At step 312, a histogram of payments for a merchant category may begenerated based on the transaction data. For example, the determinationserver may generate a histogram of payments for the merchant categoryrelated to convenience stores, or another histogram of payments may berelated to a merchant category of jewelry stores. The determinationserver may use all available transaction data related to a merchantcategory to generate the histogram of payments. Alternatively, thedetermination server may generate the histogram using a random sampling,where a subset of individuals or a sample may be chosen from alltransaction data related to the merchant category. In random sampling,each individual may be chosen randomly and entirely by chance, such thateach individual has the same probability of being chosen at any stageduring the sampling process, and each subset of k individuals has thesame probability of being chosen for the sample as any other subset of kindividuals.

FIGS. 4A-4B show example histograms of payments for determining merchantenforced transaction rules according to one or more aspects of thedisclosure. The histogram of payments in FIG. 4A may be generated usingrandom sampling with, for example, approximately 60,000 transactions.The example histogram of payments illustrated in FIG. 4A may be relatedto transactions in convenience stores. The determination server may takethese 60,000 transactions and split into ten bands of payments—forpayments under $5, between $5-10, $10-15, $15-20, $20-25, $25-30,$30-35, $35-40, $40-45 and above $45, respectively. The distribution ofpayments may be shown in each band of payments. As illustrated in FIG.4A, there are a large number of transactions having payments between$5-10, while there is a small number of transactions having paymentsunder $5.

At step 314, transaction data having purchase amounts above or below apredetermined threshold may be filtered out. The determination servermay analyze the histogram of payments and focus on the lower or higherend of the bands of payment. For example, for the histogram of paymentsrelated to convenience stores in FIG. 4A, the determination server mayattempt to determine whether the convenience stores may enforce aminimum purchase amount transaction rule. The determination server mayfilter out payment amounts above $10 and focus the analysis on bands ofpayments that are below $10, as illustrated in FIG. 4B. FIG. 4B shows ahistogram displaying ten bands of payments—for payments under $1, $1-2,$2-3, $3-4, $4-5, $5-6, $6-7, $7-8, $8-9 and $9-10, respectively. Thereare a large number of transactions having payments above $5,particularly in the band of payments between $5-6. In comparison, thereis small or minimal number of transactions in the lower five bands:under $1, $1-2, $2-3, $3-4 and $4-5. There may be slightly more under $1transactions than transactions in the other four bands between $1-5.These under $1 transactions may be the anomalies when a few cents arecharged to a card to check the transactions in some merchants in themerchant category. Based on the contrast between the fifth and sixthbands, there may be an indication that $5 may be the minimum purchaseamount. In an example to determine a maximum purchase amount, such asfor transactions originated from a jewelry store or a car dealer, thedetermination may filter out transactions below a certain paymentamount, and focus the analysis on a high end of the bands of payments.

At step 316, a first average purchase amount associated with merchantsin the merchant category may be determined. The first average purchaseamount may reflect a minimum purchase amount or a maximum purchaseamount associated with the merchants in the merchant category. Forexample, based on the bands of payments on the lower end of thehistogram of payments, the determination server may determine theminimum purchase amount for the convenience stores. As illustrated inFIGS. 4A-4B, the determination server may observe, for example, thereare a large number of transactions all share the common purchase amountof $5, while there is small or minimal number of transactions with apurchase amount that is under $5. This observation may be an indicationto the determination server that many convenience stores in thismerchant category may enforce a minimum purchase amount of $5.

At step 318, the determination server may determine a second averagepurchase amount associated with each merchant in the merchant categorybased on the first average purchase amount. For example, after thedetermination server determines that many convenience stores enforce thetransaction rule of $5 minimum purchase amount, the determination servermay use this amount as a benchmark to analyze transaction data for eachmerchant in the convenience store merchant category. For transactiondata related to a particular merchant, the determination server maycompare the transaction data with the $5 benchmark directly. Thedetermination server may also generate a histogram of payment similar tothat of FIGS. 4A-4B. If the determination server may observe that thereis small or minimum number of purchase amount that is below $5, this maybe an indication that this particular merchant may enforce thetransaction rule of the $5 minimum purchase amount for card-basedtransactions. If the determination server may observe that there are alarge amount of purchase amount that is below $5, this may be anindication that this particular merchant may not enforce the transactionrule that requires a minimum purchase amount for card-based transaction.

In a variety of embodiments, the determination server may store in theEMI database the information whether a merchant enforces a card-basedtransaction rule. The determination server may retrieve the storedinformation and present to user devices subsequently. For merchants thatmay not have corresponding information on the card-based transactionrules stored in the EMI database, the determination server may make thedeterminations on the transaction rules based on user spending patterns.

At step 320, the determination server may determine spending patternsassociated with a plurality of users using a first machine learningmodel. The determination server may use transaction data associated withmerchants in a merchant category as training data for the first machinelearning model. The first machine learning may determine spendingpatterns associated with the users based on the transaction data. Thespending patterns may include the transaction amount, the transactiontimestamp, the merchant identifier, and/or the transaction locationrelated to transactions originated from a user in a merchant category.For example, a user may purchase products from a variety of conveniencestores in her neighborhood. She may purchase a pack of gum fromconvenience store A for $1.09, a magazine from convenience store B for$3.99, and a cup of coffee and some other items for $5.09 fromconvenience store C. The first machine learning may observe the user'sspending pattern that she never spends less than $5 in store C. Thefirst machine learning model may also look at spending patterns forother users for purchases made from store C. If the machine learningmodel determines that the transactions originated from store C rarelyhave a transaction amount below $5, the transaction data may indicatethat it is likely that store C may enforce a $5 minimum purchase amountcard-based transaction rule. The determination server may submit thetransaction data related to store C to a second machine learning modelin step 322 for further processing.

At step 322, the determination server may determine that a firstmerchant (such as store C) in the merchant category enforces one or morecard-based transaction rules using a second machine learning model. Thedetermination server may determine the transaction rules related tostore C, for example, based on the spending patterns and the secondaverage purchase amount. For example, the second machine learning modelmay be trained using transaction data associated with merchants ofsimilar size to store C and/or located in a same geographic area asstore C. The determination server may determine, in step 318, aplurality of convenience stores that enforce the $5 minimum purchaseamount card-based transaction rules. The second machine learning modelmay be trained using the transaction data originating from stores thatenforce the transaction rules. The transaction data may be filtered togenerate a subset of data from stores that are of a similar size tostore C and/or located in the similar geographical area and/orsocioeconomic area as store C. The second machine learning model maytake the subset of data, the spending patterns of the users and thesecond average purchase amount (e.g. $5 minimum purchase amount) asinputs and determine whether store C may enforce the $5 minimum purchaseamount card-based transaction rules. For example, the second machinelearning may determine that store C may enforce one or more card-basedtransaction rules.

The machine learning models may be beneficial when the determinationserver needs to determine the transaction rules for a new merchant or amerchant with limited transaction data. The determination server mayextrapolate from transaction data for merchants in the same merchantcategory that may have similar merchant size and geographic locationwith those of the merchant to be analyzed.

At step 324, the determination server may determine that a user deviceis proximately located to the first merchant (e.g. store C). In avariety of embodiments, the determination server may receive ageographic location from a user device. The determination server maycompare the geographic location with a predefined geofence associatedwith the first merchant (e.g. store C) and determine that the userdevice has come into the vicinity of store C or may be inside store C.

In a variety of embodiments, the determination server may detect thatthe user device has connected to a wireless network associated with thefirst merchant (e.g. store C) for a predetermined period of time. Forexample, after detecting that the user device has connected to awireless network of store C for ten seconds, the determination servermay take this as an indication that the user device is located withinstore C.

At step 326, a notification may be sent to the user device indicatingthe one or more card-based transaction rules associated with the firstmerchant (e.g. store C). The determination server may provide a userinterface indicating the transaction rules after detecting that the userdevice is proximately located to store C or inside store C. Thedetermination server may provide a search page on the user device andthe user may search for the transaction rules associated with store Cand/or stores nearby. For example, FIG. 6A illustrates an example searchpage that may be presented to user device 600. The user may enter asearch query to provide a name of the store or a location in field 605.The determination server may respond to the search query and present therules on the search page. In the example of FIG. 6B, the determinationserver may display a map 610, such as Google map on the user device. Themap may display a current location of a user or user device, forexample, at the intersection of North Avenue and First Street. The mapmay also display four stores, store A, B, C and D in a geographic areaof the user device. The user may be presented with an option to click ona store to see the card-based transaction rules that the store mayimpose. For example, the user may click on store C and the transactionrules may be displayed on the map with store C. The determination servermay display other stores on that map that may be in the vicinity ofstore C, such as store A, B or C. At least some of these other storesmay not enforce the same card-based transaction rules as store C. Assuch, the user may have an option to go to an alternative store thatdoes not enforce these rules.

FIGS. 7A-7B show example notifications displayed on a user deviceaccording to one or more aspects of the disclosure. In FIG. 7A, anotification 710 may be sent to a user device listing a plurality ofstores (e.g. Stores 1-12) in the geographic area of the user device.These stores may enforce one or more transaction rules, for example, aminimum purchase amount of $5. The notification 710 may provide anoption to display alternative stores that do not enforce such rules. Ifthe user selects the option to see the alterative stores that do notenforce the card-based transaction rules, stores 13-18 may be displayedin FIG. 7B. FIG. 7B may also provide an additional option 720 to viewmore stores in the geographic area that do not enforce the card-basedtransaction rules.

FIG. 5 shows a flow chart of a process of for determining merchantenforced transaction rules according to one or more aspects of thedisclosure. Some or all of the steps of process 500 may be performedusing one or more computing devices as described herein.

At step 510, a first machine learning model may be trained usingtransaction data associated with one or more merchants in a merchantcategory. A determination server may attempt to determine whether aparticular merchant (e.g. a car dealer) enforces one or more card-basedtransaction rules such as only certain credit cards (e.g. Visa Card orMaster Card) may be accepted, but other cards (e.g. Discover Card,American Express or international card) may not be accepted. Thedetermination server may retrieve from a transaction database and/or EMIdatabase, transaction records related to merchant category code, forexample, MCC No. 5511 for car dealers. The first machine learning modelmay be trained using relevant transaction data associated with MCC No.5511.

At step 520, the first machine learning model may determine spendingpatterns from a plurality of users associated with the merchants in themerchant category. The spending patterns may include the credit/debitcard type that the user used to make the purchase, the transactiontimestamp, the merchant identifier, and/or the transaction locationrelated to transactions originated from users in the merchant category.For example, a first user may purchase a used car from car dealer Ausing a Visa Card. A second user may purchase a car from car dealer Ausing a Master Card. A third user may purchase a car from car dealer Busing a Discover Card. A fourth user may purchase a new car from dealerC using a Visa card. If the machine learning model determines that thetransactions originated from dealer A has no, or minimal number, oftransactions using a Discover Card, the transaction data may indicatethat it is possible that dealer A may enforce a card-based transactionrule that does not allow Discover Card. The determination server maysubmit the transaction data related to dealer A to a second machinelearning model for further processing.

At step 530, the second machine learning model may be trained usingtransaction data associated with merchants of similar (comparable) sizeto the particular merchant (e.g. dealer A) and/or located in ageographic area and/or socioeconomic area of the particular merchant.The determination server may determine the transaction rules related todealer A, for example, based on the spending patterns. There may belimited transaction data related to dealer A, and the second machinelearning model may extrapolate from transaction data originated fromother car dealers of similar size to dealer A and/or located in the same(or similar) geographic area and/or socioeconomic area of dealer A.

At step 540, the second machine learning model may determine whether theparticular merchant (e.g. dealer A) in the merchant category enforcesone or more card-based transaction rules. The transaction data may befiltered to generate a subset of data from car dealers that have similarsize of dealer A and/or are located in a similar geographic and/orsocioeconomic area of dealer A. The second machine learning model maytake the subset of data, the spending patterns of the users, and/or thecard types as inputs and determine whether dealer A may enforce thecard-based transaction rule that allow certain cards, while rejectingother cards. For example, the second machine learning may determine thatdealer A may enforce a card-based transaction rule that only accept VisaCard or Master Card.

The techniques described herein may be used to determine merchantenforced card-based transaction rules. By using random sampling andgenerating a histogram of payments, the determination server may filterout irrelevant transaction data and focus the analysis on the bands ofpayments of interest. The determination server may use machine learningmodels to determine whether a merchant enforce transaction rules,particularly when there is limited transaction data originated from themerchant. The determination server may use geofence or wirelessconnectivity to determine a location of the user device and send anotification to the user device alerting such transaction rules andoffer alternatives.

One or more aspects discussed herein may be embodied in computer-usableor readable data and/or computer-executable instructions, such as in oneor more program modules, executed by one or more computers or otherdevices as described herein. Generally, program modules includeroutines, programs, objects, components, data structures, and the likethat perform particular tasks or implement particular abstract datatypes when executed by a processor in a computer or other device. Themodules may be written in a source code programming language that issubsequently compiled for execution, or may be written in a scriptinglanguage such as (but not limited to) HTML or XML. The computerexecutable instructions may be stored on a computer readable medium suchas a hard disk, optical disk, removable storage media, solid-statememory, RAM, and the like. As will be appreciated by one of skill in theart, the functionality of the program modules may be combined ordistributed as desired in various embodiments. In addition, thefunctionality may be embodied in whole or in part in firmware orhardware equivalents such as integrated circuits, field programmablegate arrays (FPGA), and the like. Particular data structures may be usedto more effectively implement one or more aspects discussed herein, andsuch data structures are contemplated within the scope of computerexecutable instructions and computer-usable data described herein.Various aspects discussed herein may be embodied as a method, acomputing device, a system, and/or a computer program product.

Although the present invention has been described in certain specificaspects, many additional modifications and variations would be apparentto those skilled in the art. In particular, any of the various processesdescribed above may be performed in alternative sequences and/or inparallel (on different computing devices) in order to achieve similarresults in a manner that is more appropriate to the requirements of aspecific application. It is therefore to be understood that the presentinvention may be practiced otherwise than specifically described withoutdeparting from the scope and spirit of the present invention. Thus,embodiments of the present invention should be considered in allrespects as illustrative and not restrictive. Accordingly, the scope ofthe invention should be determined not by the embodiments illustrated,but by the appended claims and their equivalents.

What is claimed is:
 1. A computer-implemented method comprising:receiving, via an electronic payment network and from a plurality ofusers, transaction data associated with a plurality of merchants;generating a histogram of payments associated with a merchant categorybased on the transaction data: filtering out, based on an analysis ofthe histogram of payments, transaction data having purchase amountsabove or below a predetermined threshold; determining a first averagepurchase amount associated with merchants in the merchant category;determining, based on the first average purchase amount, a secondaverage purchase amount associated with each merchant in the merchantcategory; determining, using a first machine learning model, spendingpatterns associated with the plurality of users; based on the spendingpatterns and based on the second average purchase amount, determining,using a second machine learning model, that a first merchant in themerchant category enforces one or more card-based transaction rules;determining that a user device, associated with a first user of theplurality of users, is proximately located to the first merchant; andsending, to the user device associated with the first user, anotification indicating the one or more card-based transaction rulesassociated with the first merchant.
 2. The computer-implemented methodof claim 1, wherein determining that the first merchant in the merchantcategory enforces the one or more card-based transaction rulescomprises: detecting, based on the transaction data, a patternassociated with a plurality of transactions that each share a commonpayment amount; and determining that the first merchant in the merchantcategory enforces the one or more card-based transaction rules based onthe common payment amount.
 3. The computer-implemented method of claim1, further comprising: storing, in a merchant database, the one or morecard-based transaction rules associated with the first merchant.
 4. Thecomputer-implemented method of claim 1, wherein the one or morecard-based transaction rules comprises at least one of: a minimumpurchase amount; a maximum purchase amount; or a surcharge forcard-based transactions.
 5. The computer-implemented method of claim 1,wherein determining that the user device is proximately located to thefirst merchant comprises: receiving, from the user device, a geographiclocation associated with the user device; and comparing the geographiclocation with a predefined geofence associated with the first merchant.6. The computer-implemented method of claim 1, wherein determining thatthe user device is proximately located to the first merchant comprises:detecting that the user device has connected to a wireless networkassociated with the first merchant for a predetermined period of time.7. The computer-implemented method of claim 1, further comprising:training the first machine learning model using transaction dataassociated with one or more merchants in the merchant category.
 8. Thecomputer-implemented method of claim 1, further comprising: training thesecond machine learning model using transaction data associated withmerchants of similar size of the first merchant and located in ageographic location of the first merchant.
 9. The computer-implementedmethod of claim 1, further comprising: receive, from the user device, ageographic location associated with the user device; and sending, to theuser device, a second notification indicating a plurality of merchantsin the geographic location that do not enforce the one or morecard-based transaction rules.
 10. A server comprising: one or moreprocessors; and memory storing instructions that, when executed by theone or more processors, cause the server to: receive, via an electronicpayment network and from a plurality of users, transaction dataassociated with a plurality of merchants; determine a first averagepurchase amount associated with merchants in a merchant category;determine, based on the first average purchase amount, a second averagepurchase amount associated with each merchant in the merchant category;train a first machine learning model using transaction data associatedwith merchants in the merchant category; determine, using the firstmachine learning model, spending patterns associated with the pluralityof user; train a second machine learning model using transaction dataassociated with merchants of similar size of a first merchant andlocated in a geographic location of the first merchant; based on thespending patterns and based on the second average purchase amount,determine, using the second machine learning model, whether the firstmerchant in the merchant category enforces one or more card-basedtransaction rules; determine that a user device associated with a firstuser of the plurality of users, is proximately located to the firstmerchant; and send, to the user device associated with the first user, anotification indicating the one or more card-based transaction rulesassociated with the first merchant.
 11. The server of claim 10, whereinthe instructions, when executed by the one or more processors, cause theserver to: prior to determining the first average purchase amount,generate a histogram of payments associated with the merchant categorybased on the transaction data: and filter out, based on an analysis ofthe histogram of payments, transaction data having purchase amountsabove or below a predetermined threshold.
 12. The server of claim 10,wherein the instructions, when executed by the one or more processors,cause the server to: store, in a merchant database, the one or morecard-based transaction rules associated with the first merchant.
 13. Theserver of claim 10, wherein the one or more card-based transaction rulescomprise at least one of: a minimum purchase amount; a maximum purchaseamount; or a surcharge for card-based transactions.
 14. The server ofclaim 10, wherein the instructions, when executed by the one or moreprocessors, cause the server to: receive, from the user device, ageographic location associated with the user device; and compare thegeographic location with a predefined geofence associated with the firstmerchant.
 15. The server of claim 10, wherein the instructions, whenexecuted by the one or more processors, cause the server to: detect thatthe user device has connected to a wireless network associated with thefirst merchant for a predetermined period of time.
 16. The server ofclaim 10, wherein the instructions, when executed by the one or moreprocessors, cause the server to: receive, from the user device, ageographic location associated with the user device; and send, to theuser device, a second notification indicating a plurality of merchantsin the geographic location that do not enforce the one or morecard-based transaction rules.
 17. One or more non-transitory mediastoring instructions that, when executed, cause a computing device to:receive, via an electronic payment network and from a plurality ofusers, transaction data that is associated with a plurality ofmerchants; determine a first average purchase amount associated withmerchants in a merchant category; determine, based on the first averagepurchase amount, a second average purchase amount associated with eachmerchant in the merchant category; determining, using a first machinelearning model, spending patterns associated with the plurality ofusers; based on the spending patterns and based on the second averagepurchase amount, determining, using a second machine learning model,whether a first merchant in the merchant category enforces one or moretransaction card-based rules; receive, from a user device, associatedwith a first user of the plurality of users, a geographic locationassociated with the user device; compare the geographic location with apredefined geofence associated with the first merchant; determining thatthe user device is proximately located to the first merchant; andsending, to the user device associated with the first user, anotification indicating the one or more transaction card-based rulesassociated with the first merchant.
 18. The non-transitory media ofclaim 17, wherein the instructions, when executed, cause the computingdevice to: generate a histogram of payments associated with the merchantcategory based on the transaction data: filter out, based on an analysisof the histogram of payments, transaction data having purchase amountsabove or below a predetermined threshold.
 19. The non-transitory mediaof claim 17, wherein the instructions, when executed, cause thecomputing device to: store, in a merchant database, the one or morecard-based transaction rules associated with the first merchant.
 20. Thenon-transitory media of claim 17, wherein the one or more card-basedtransaction rules comprise at least one of: a minimum purchase amount; amaximum purchase amount; or a surcharge for card-based transactions.